Advanced Optimization Strategies for UAV Mission Planning and Operation

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Artificial Intelligence in Drones (AID)".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1491

Special Issue Editors


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Guest Editor
Department of Industrial Engineering (DIN), University of Bologna, 47121 Forlì, Italy
Interests: flight mechanics; aircraft design; performance analysis and optimization; battery-powered vehicles; state estimation; flight testing; model-based design; modeling, simulation, and control; underactuated systems; spacecraft attitude control
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Industrial Engineering (DIN), University of Bologna, 47121 Forlì, Italy
Interests: flight mechanics; aircraft design; performance analysis and optimization; battery-powered vehicles; state estimation; flight testing; model-based design; modeling, simulation, and control; underactuated systems; spacecraft attitude control
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering for Innovation (DII), University of Salento, 73100 Lecce, Italy
Interests: flight mechanics; aircraft design; performance analysis and optimization; battery-powered vehicles; state estimation; flight testing; model-based design; modeling, simulation, and control; underactuated systems; spacecraft attitude control

Special Issue Information

Dear Colleagues,

The growing adoption of unmanned aerial vehicles (UAVs) across sectors such as logistics, environmental monitoring, disaster response, and infrastructure inspection has introduced increasingly complex operational challenges. Modern UAV operations, ranging from single-platform missions involving multiple payload deliveries to large-scale coordinated fleets, demand efficient and reliable solutions for mission planning and execution. These scenarios often require optimizing multiple interdependent objectives, including task scheduling, trajectory planning, payload management, energy consumption, and communication reliability, while operating under dynamic, uncertain, and resource-constrained environments. To address these challenges, a wide spectrum of optimization techniques have been explored, spanning exact mathematical programming, heuristic and metaheuristic algorithms, hybrid frameworks, and adaptive real-time strategies. This Special Issue focuses on recent advances in optimization methods for complex UAV operations, aiming to highlight innovative models, algorithms, and architectures that improve efficiency, scalability, and robustness. We encourage submissions that present theoretical contributions, computational frameworks, and real-world implementations demonstrating enhanced mission performance. By integrating insights from control theory, operations research, autonomous systems, and distributed decision-making, this collection seeks to advance the state of the art in UAV mission planning and foster practical solutions for next-generation aerial operations across diverse and evolving applications.

This Special Issue invites manuscripts addressing, but not limited to, the following themes:

- Mission planning and scheduling for UAV operations;

- Trajectory and path optimization;

- Payload allocation and management;

- Multi-UAV coordination and task assignment;

- Energy-efficient and resource-constrained UAV missions;

- Optimization algorithms: heuristic, metaheuristic, and hybrid methods;

- Autonomous decision-making and real-time adaptive strategies;

- Applications in logistics, environmental monitoring, and disaster response.

We look forward to receiving your original research articles and reviews.

Dr. Emanuele Luigi de Angelis
Prof. Dr. Fabrizio Giulietti
Prof. Dr. Giulio Avanzini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned aerial vehicles (UAVs)
  • autonomous aerial systems
  • mission planning and scheduling
  • trajectory optimization
  • payload allocation and optimization
  • multi-UAV coordination
  • optimization algorithms
  • heuristic and metaheuristic methods
  • energy-efficient UAV operations
  • autonomous decision-making

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Published Papers (3 papers)

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Research

32 pages, 13734 KB  
Article
Objective Programming Partitions and Rule-Based Spanning Tree for UAV Swarm Regional Coverage Path Planning
by Bangrong Ruan, Tian Jing, Meigen Huang, Xi Ning, Jiarui Wang, Boquan Zhang and Fengyao Zhi
Drones 2026, 10(1), 60; https://doi.org/10.3390/drones10010060 - 14 Jan 2026
Viewed by 228
Abstract
To address the problem of regional coverage path planning for unmanned aerial vehicle swarms (UAVs), this study proposes an algorithm based on objective programming partitions (OPP) and rule-based spanning tree coverage (RSTC). Aiming at the shortcomings of the traditional Divide Areas based on [...] Read more.
To address the problem of regional coverage path planning for unmanned aerial vehicle swarms (UAVs), this study proposes an algorithm based on objective programming partitions (OPP) and rule-based spanning tree coverage (RSTC). Aiming at the shortcomings of the traditional Divide Areas based on Robots Initial Positions combined with Spanning Tree Coverage (DARP-STC) algorithm in two core stages, that is, region partitions and spanning tree generation, the proposed algorithm conducts a targeted design and optimization, respectively. In the region partition stage, an objective programming and 0–1 integer programming model are adopted to realize the balanced allocation of UAVs’ task regions. In the spanning tree generation stage, a rule is designed to construct a spanning tree of coverage paths and is proven to achieve the minimum number of turns for the UAV under certain conditions. Both simulations and physical experiments demonstrate that the proposed algorithm can not only significantly reduce the number of turns of UAVs but also enhance the efficiency and coverage degree of tasks for UAV swarms. Full article
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22 pages, 899 KB  
Article
Rapid MRTA in Large UAV Swarms Based on Topological Graph Construction in Obstacle Environments
by Jinlong Liu, Zexu Zhang, Shan Wen, Jingzong Liu and Kai Zhang
Drones 2026, 10(1), 48; https://doi.org/10.3390/drones10010048 - 9 Jan 2026
Viewed by 224
Abstract
In large-scale Unmanned Aerial Vehicle (UAV) and task environments—particularly those involving obstacles—dimensional explosion remains a significant challenge in Multi-Robot Task Allocation (MRTA). To this end, a novel heuristic MRTA framework based on Topological Graph Construction (TGC) is proposed. First, the physical map is [...] Read more.
In large-scale Unmanned Aerial Vehicle (UAV) and task environments—particularly those involving obstacles—dimensional explosion remains a significant challenge in Multi-Robot Task Allocation (MRTA). To this end, a novel heuristic MRTA framework based on Topological Graph Construction (TGC) is proposed. First, the physical map is transformed into a pixel map, from which a Generalized Voronoi Graph (GVG) is generated by extracting clearance points, which is then used to construct the topological graph of the obstacle environment. Next, the affiliations of UAVs and tasks within the topological graph are determined to partition different topological regions, and the task value of each topological node is calculated, followed by the first-phase Task Allocation (TA) on these topological nodes. Finally, UAVs within the same topological region with their allocated tasks perform a local second-phase TA and generate the final TA result. The simulation experiments analyze the influence of different pixel resolutions on the performance of the proposed method. Subsequently, robustness experiments under localization noise, path cost noise, and communication delays demonstrate that the total benefit achieved by the proposed method remains relatively stable, while the computational time is moderately affected. Moreover, comparative experiments and statistical analyses were conducted against k-means clustering-based MRTA methods in different UAV, task, and obstacle scale environments. The results show that the proposed method improves computational speed while maintaining solution quality, with the PI-based method achieving speedups of over 60 times and the CBBA-based method over 10 times compared with the baseline method. Full article
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28 pages, 5368 KB  
Article
Dynamic Estimation of Formation Wake Flow Fields Based on On-Board Sensing
by Tianhui Guo, Tielin Ma, Haiqiao Liu, Jingcheng Fu, Bingchen Cheng and Lulu Tao
Drones 2025, 9(11), 798; https://doi.org/10.3390/drones9110798 - 17 Nov 2025
Viewed by 650
Abstract
Close formation flight is a practical strategy for fixed-wing unmanned aerial vehicle (UAV) swarms. Maintaining UAVs at aerodynamically optimal positions is essential for efficient formation flight. However, aerodynamic optimization methods based on computational fluid dynamics (CFD) are computationally intensive and difficult to apply [...] Read more.
Close formation flight is a practical strategy for fixed-wing unmanned aerial vehicle (UAV) swarms. Maintaining UAVs at aerodynamically optimal positions is essential for efficient formation flight. However, aerodynamic optimization methods based on computational fluid dynamics (CFD) are computationally intensive and difficult to apply in real time for large-scale formations. Inspired by bio-formation flight, this study proposes an on-board sensing-based method for wake flow field estimation, with potential for extension to complex formations. The method is based on a parameter identification-induced velocity model (PI-Model), which uses only onboard sensors, including two lateral air data systems (ADS), to sample the wake field. By minimizing the residual of the induced velocity, the model identifies key parameters of the wake and provides a dynamic estimation of the wake velocity field. Comparisons between the PI-Model and CFD simulations show that it achieves higher accuracy than the widely used single horseshoe vortex model in both wake velocity and aerodynamic effects. Applied to a two-UAV formation scenario, CFD validation confirms that the trailing UAV achieves a 15–25% drag reduction. These results verify the effectiveness of the proposed method for formation flight and demonstrate its potential for application in complex, dynamic multi-UAV formations. Full article
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